In TABLE REF  we provide the reconstruction results, training datasets, and model parameters of these models (lightweight models and large models are separated by the bold black line). According to the results, we can find that: (1) using a large dataset (e.g., DIV2K+Flickr2K) can make the model achieve better results; (2) it is not entirely correct that the more model parameters, the better the model performance. This means that unreasonably increasing the model size is not the best solution; (3) Transformer-based models show strong advantages, whether in lightweight models (e.g., ESRT [1]}) or large models (e.g., SwinIR [2]}); (4) research on the tiny model (parameters less than 1000K) is still lacking. In the future, it is still important to explore more discriminative evaluation indicators and develop more effective
SISR models.
